Defibrillator with shock energy based on EKG transform

ABSTRACT

A method for detecting a heart disorder comprises examination of a phase-plane plot (PPP) of a patient electrocardiogram (EKG). The PPP&#39;s degree of deterministic chaos may be measured by a processor. Analysis of the PPP may indicate a propensity for fibrillation that is, indicate both the risk of fibrillation and its actual onset (cases where risk is 100 percent). A second method for detecting a heart disorder comprises examination of a frequency-domain transform (such as an FFT) of a patient EKG. An automatic defibrillating device may comprise means for delivering a variable shock, the size of which is determined at least in part by the FFT&#39;s peak energy. A method for detecting drug toxicity comprises examination of a parameter time constant for an action-potential duration (APD) restitution curve which is constructed for the patient.

This is a Division application of Ser. No. 08/191,099 now U.S. Pat. No.5,555,889 filed Feb. 4, 1994 which in turn is a continuation ofapplication Ser. Nos. 07/701,753 filed on May 17, 1991 and 07/716,665,filed Jun. 4, 1991, respectively a continuation-in-part and a divisionalof Ser. No. 07/541,881 filed on Jun. 20, 1990) abandoned. Thisapplication declares its priority from these applications which are alsoincorporated by reference. This application combines the disclosures ofthese applications.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates to heart disorders. More specifically, thisinvention relates to detecting and evaluating arrhythmia, fibrillationand related disorders by manipulation of an electrocardiogram signal.

2. Description of Related Art

Despite major advances in the diagnosis and treatment of ischemic heartdisease over the past decade, a substantial number of patients each yearmay suffer sudden cardiac death as a consequence of ventricularfibrillation (VF). To date, no reliable predictive or preventivemeasures have been developed. By all outward appearances, VF is a highlycomplex, seemingly random phenomenon. So are other related heartdisorders, including those stages in heart behavior which typicallyprecede JF (onset of VF). Accordingly, it is difficult for automateddevices to determine with any reliability that a patient is undergoingVF or onset of VF. Moreover, onset of VF may also be difficult todetermine with any reliability, even for skilled medical personnel.

A method of detecting and evaluating heart disorders would thereforefind wide applicability and utility. Patient monitoring devices maysummon medical personnel if the patient is undergoing VF or onset of VF.Automatic devices which attempt to counter VF, e.g. automaticimplantable cardiac defibrillators (AICDs) may vary their operationbased on evaluation of the severity of the patient's condition. Methodsfor reliably evaluating the risk of VF may also have important utilityin monitoring patients undergoing surgery or other critical therapy.

It has been found that some anti-arrhythmic drugs may also have apro-arrhythmic effect in excess concentrations. For example, quinidinehas been known to be toxic in this manner. A method of detecting andevaluating heart disorders would also have wide applicability andutility in determining if a patient has been subjected to a toxic (orpartially toxic) dosage of a drug relating to heart condition.

Chaos theory is a recently developed field relating to phenomena whichappear to be highly complex and seemingly random, but which may bedescribed as the deterministic result of relatively simple systems.Chaos theory may have potentially wide applications in biologic andother systems involving ambiguity and uncertainty. For example, it hasbeen conjectured that chaos theory may be valuable for describingcertain natural processes, including electroencephalogram (EEG) andelectrocardiogram (EKG) signals. Techniques for detecting and evaluatingaspects of deterministic chaos are known in the field of chaos theory,but have found little application in the medical field.

Accordingly, there is a need for improved methods and devices fordetecting and evaluating heart disorders, including ventricularfibrillation (VF) and the onset of VF.

SUMMARY OF THE INVENTION

A first aspect of the invention provides a method for detecting a heartdisorder, by examination of a phase-plane plot (PPP) of a patientelectrocardiogram (EKG). A normal patient will have a PPP which isrelatively smooth; a patient at risk of developing ventricularfibrillation (VF) onset will have a PPP which exhibits features of achaotic process, such as multiple bands, "forbidden zones", periodicitywith period-doubling and phase locking; a patient exhibiting VF willhave a PPP which appears noisy and irregular. Differing PPPs may bereadily recognized, thus detecting patients with heart disorders.

In a preferred embodiment, the PPP's degree of deterministic chaos maybe measured by a processor, such as by graphic and numeric analysis. (1)The processor may measure a Lyapunov exponent or a fractal dimension ofthe PPP. (2) The processor may determine a Poincare section of the PPPand examine that Poincare section for indicators of deterministic chaos.Also, the processed PPP and Poincare sections may be reviewed by a humanoperator. The processed PPP and Poincare sections may indicate thepropensity for fibrillation.

A second aspect of the invention provides a method for detecting a heartdisorder, by examination of a frequency-domain transform (such as anFFT) of a patient EKG. A normal patient will have an FFT with a discretespectrum, while a patient exhibiting VF will have an FFT with arelatively continuous spectrum and a peak energy at a relatively lowfrequency (e.g., about 5-6 Hz). A patient exhibiting VF which isdifficult to revert with shock will have an FFT with a peak energy at arelatively high frequency (e.g., about 10 Hz or more).

In a preferred embodiment, an automatic defibrillating device maycomprise means for delivering a variable shock, the size of which isdetermined at least in part by the FFT's peak energy. The defibrillatingdevice may also comprise means for signalling an alarm if the FFT's peakenergy is at a relatively high frequency.

A third aspect of the invention provides a method for detecting drugtoxicity, based on particulars of an action potential duration (APD)restitution curve, or an action-potential amplitude (APA) curve, whichis constructed for the patient, such as fitting an exponential relationto that curve or such as a parameter time constant for that curve. Theslope of the fitted curve will indicate the patient's possibility ofpredisposition to arrhythmia. Differences in the parameters of thefitted curve allow one to distinguish between normal and abnormalpatients, e.g. those at risk of arrhythmia or ischemia. A normal patientwill have a relatively low parameter time constant; a patient who isexhibiting drug toxicity will have a relatively high parameter timeconstant. A PPP of APD or APA data may also be generated, and theanalytical techniques described herein may be utilized to interpret thatPPP, to determine and evaluate drug toxicity.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a patient monitoring system.

FIG. 2 shows a set of sample EKG signals.

FIG. 3 shows a set of corresponding PPPs for the sample EKG signals ofFIG. 2. FIG. 3A shows a detail of the "funnel" area of the PPPcorresponding to the third EKG of FIG. 2, taken from a patientexhibiting VF.

FIG. 4 shows an example PPP and a corresponding Poincare section.

FIG. 5 shows an example PPP and a corresponding time-lapse Poincaresection.

FIG. 6A shows the frequency-domain, fast Fourier transform of an EKGfrom a normal patient. FIG. 6b shows the frequency-domain fast Fouriertransform of an EKG from a patient experiencing VF.

FIG. 7 shows an improved automatic implantable cardiac defibrillator("AICD").

FIG. 8 shows a signal response of an individual heart muscle cell to astimulus, known in the art as "action potential".

FIG. 9 shows a flow chart for a method of registering a propensity forfibrillation by calculating a fractal dimension using a box countingmethod.

FIG. 10 shows a flow chart for a method of registering a propensity forfibrillation by determining correlation dimension convergence.

DESCRIPTION OF THE PREFERRED EMBODIMENT I

A first aspect of the invention relates to detection and evaluation ofheart disorders by examination of a phase-plane plot (PPP) of a patientelectrocardiogram (EKG).

FIG. 1 shows a patient monitoring system. A patient 101 is coupled to anelectrocardiogram (EKG) device 102, which acquires EKG signals andtransmits them to a processor 103. The processor 103 may display the EKGsignals on a monitor 104 (as is well-known in the art), or it mayprocess the EKG signals and display any results of processing on themonitor 104.

EKG signals are well-known in the art, as are methods of acquiring them.As used herein, an EKG refers to a surface electrocardiogram, but otherforms of electrocardiogram would also work with the methods disclosedherein, and are within the scope and spirit of the invention. Forexample, the EKG shown herein may comprise a surface EKG, an epicardialEKG, an endocardial EKG, or another related signal (or set of signals)measured in or near the heart. Moreover, the signal which is manipulatedmay be a voltage signal, a current signal, or another relatedelectromagnetic values (or set of values).

FIG. 2 shows a set of sample EKG signals. A first EKG signal 201 shows anormal patient. A second EKG signal 202 shows a patient in transition toVF. A third EKG signal 203 shows a patient with VF.

The processor 103 may construct a phase-plane plot (PPP) from the EKGsignal. A first type of PPP comprises a plot of an EKG variable againstits first derivative. In a preferred embodiment, the EKG variable isvoltage, v (itself a function of time); its first derivative is dv/dt(also a function of time).

However, it would be clear to one of ordinary skill in the art, afterperusal of the specification, drawings and claims herein, that widelatitude in construction of the PPP is possible. The variable chosen forthe PPP may be any one of a variety of different parameters, includingEKG voltage, current, or another signal value. The chosen variable (v)may be plotted against its first time derivative (dv/dt), its secondtime derivative d² v/dt², or another time derivative d^(n) v/dt^(n). Or,an Mth derivative may be plotted against an Nth derivative.

Another type of PPP may comprise a plot of an EKG variable (or an Nthderivative thereof) against a time delayed version of itself, (e.g. v(t)versus v(t-δt)). This type of PPP is sometimes also called a "returnmap". This type of PPP is led sensitive to EKG signal noise.

Another type of PPP may comprise a plot of three EKG variables (or Nthderivatives thereof) simultaneously (e.g., v, dv/dt, and d² v/dt²). Sucha PPP would be 3-dimensional. Where the PPP is 3-dimensional, it may bedisplayed stereoscopically, or a 2-dimensional plane "cut" of the3-dimensional display may be displayed on a 2-dimensional display. Itwould be clear to one of ordinary skill in the art, that all of thesechoices described herein, or combinations thereof, would be workable,and are within the scope and spirit of the invention.

FIG. 3 shows a set of corresponding PPPs for the sample EKG signals ofFIG. 2. A first PPP 301 corresponds to the first EKG signal 201. Asecond PPP 302 corresponds to the second EKG signal 202. A third PPP 303corresponds to the third EKG signal 203. FIG. 3a shows a detail of the"funnel" area at the PPP corresponding to the third EKG of FIG. 2, takenfrom a patient exhibiting VF.

The funnel area of the PPP, shown in FIG. 3a, in particular, exhibits anirregular and highly complex pattern, indicative of ventricularfibrillation to even a relatively untrained eye.

Part of this aspect of the invention is the discovery that a normalpatient will have a PPP which exhibits the regularity and smoothness ofan EKG signal from that normal patient, while a patient undergoing VFwill have a PPP which exhibits the irregularity and complexity of an EKGsignal which might be deterministic chaos (e.g., a periodicity, bandingand "forbidden zones"). Moreover, a patient in transition from normalinto VF (i.e., in VF onset) exhibits a PPP which is consistent with anassessment that the EKG signal for the patient is in transition todeterministic chaos.

A normal patient has a relatively regular beat-to-beat EKG signal. Asthe patient transitions to VF, the patient's EKG signal at first showsoscillations between pairs of alternant regular beat-to-beat signals. Asthe transition continues, the patient's EKG signal then showsoscillations between greater and greater numbers of alternant regularsignals (e.g., four possible alternants, eight possible alternants,etc.), until it is no longer possible to identify alternant regularsignals and the EKG signal is irregular and highly complex. At thatpoint, the patient is generally said to be exhibiting VF.

In like manner, the patient's PPP will transition from a smoothsingle-banded display, through a multi-banded display (showing multiplealternants) and finally to an irregular and highly complex display. Thedisplay change in the PPP is so striking that even a relativelyuntrained person can see the difference. This is in contrast withdisplay changes in the EKG, which generally requires a skilledcardiologist to evaluate.

There are several possible factors which might cause a patient totransition from normal to VF. These factors may include drug overdose(especially overdose with an antiarrhythmic which has a pro-arrhythmiceffect in overdosage, e.g., quinidine intoxication), excessiveelectrical stimulation, hypothermia, ischemia, and stress. In apreferred embodiment, a patient monitor may examine the patient's PPP soas to determine if the patient is in transition from normal to VF; thiscould indicate that one of these pro-arrhythmic factors is excessivelypresent.

The processor 103 may further process the PPP so as to measure the PPP'sdegree of deterministic chaos. Several techniques may be applied forthis purpose:

(1) The processor 103 may measure a Lyapunov exponent of the PPP. TheLyapunov exponent of the PPP is a measure of the degree to which nearbypaths of the PPP diverge. The Lyapunov exponent is well-known in chaostheory and may be measured with available software. See, e.g., Wolf etal., "Determining Lyapunov exponents from a time series", Physica D1985;16:285-317.

(2) The processor 103 may measure a fractal dimension of the PPP. Thefractal dimension of the PPP is a measure of the degree to which the PPPforms a "space-filling" curve. The fractal dimension is well-known inchaos theory and may be measured with several techniques (e.g.correlation dimension or box-counting methods), for example as shownbelow: and in FIG. 9.

After the EKG is sensed 901 and the phase-plane plot constructed 902, inorder to measure the fractal dimension of the PPP the processor of FIG.1 superimposes a rectilinear grid (comprising a set of boxes) 903 on thePP 903 and counts the number of boxes which are cut by the PPP's trace904. The processor of FIG. 1 then varies the size of the grid andrecords each grid size and each count 905. The processor FIG. 1 thencomputes the constant k in the following relation 906: In (# of boxescut)=k*ln (# of boxes in grid).

The constant k is a measure of the fractal dimension of the PPP. A valueof k between about 3 and 7, especially with a fractional component,implies that the PPP is likely to represent a process based ondeterministic chaos, and therefore a patient who is close to (oractually in) VF 907. The propensity for ventricular fibrillation is thenregistered 908.

The fractal dimension may also be measured with correlation dimensiontechniques such as shown in FIG. 10 and appendix pp. 29-30. In thisprocess after the EKG is sensed 1001 and the phase-plane plotconstructed 1002 the processor of FIG. 1 applies a modifiedGrossberger-Procaccia algorithm 1003. The correlation dimension is thenevaluated for convergence 1004. In the event of convergence thepropensity for ventricular fibrillation is registered 1005.

(3) The processor 103 may determine a Poincare section of the PPP andexamine that Poincare section for indicators of deterministic chaos, asdescribed herein. The processed PPP and Poincare sections may also bedisplayed for review by a human operator, whereupon any visiblestructure will be readily recognized.

FIG. 4 shows an example PPP 401 and a corresponding Poincare section402. A Poincare section may comprise a line segment drawn across a partof the PPP. In general, such a line segment will be close toperpendicular to the trajectories of the PPP in a region of interest.

The processor 103 may acquire the data points in each Poincare sectionor PPP and compute a statistical measure of anisotropy or inhomogeneityof those data points. One such measure is based on the mean and standarddeviation of those data points (these may be computed by statisticalmethods which are well-known in the art). The ratio

    r=(standard deviation)/(expected value)                    (403)

is a measure of the degree of clumping in the Poincare section.

A greater value for r implies that the PPP is more likely to represent aprocess based on deterministic chaos, and therefore a patient who isclose to (or actually in) VF. The value for r may be displayed forreview by a human operator in comparison with a value for r for a normalpatient, together with a set of confidence bands, as is well-known inthe art, for indicating a degree of variation from a normal patient.

The processor 103 may also compute other statistical measures of thePoincare section.

The processor 103 may also determine a "time-lapse" Poincare section ofthe PPP.

FIG. 5 shows an example PPP 501 and a corresponding time-lapse Poincaresection 502. A time-lapse Poincare section may comprise a set of datapoints selected from the PPP by selecting one data point every tseconds. The time-lapse Poincare section may be analyzed in like manneras the other Poincare section disclosed herein.

II

A second aspect of the invention relates to detection and evaluation ofheart disorders based on a frequency-domain transform of a patient EKG.

FIG. 6 shows a set of corresponding frequency-domain transforms,obtained by performing an FFT on the EKG signal. A first transform 601corresponds to a first EKG signal (not shown). A second transform 602corresponds to a second EKG signal (not shown).

In the first transform 601 of FIG. 6a, representing a normal patient,the frequency spectrum shows that the energy of the corresponding EKGsignal occurs primarily at a discrete set of frequencies. In the secondtransform 602 of FIG. 6b, representing a patient exhibiting VF, thefrequency spectrum shows that the energy of the corresponding EKG signalhas a continuous spectrum of frequencies, and has an energy peak 603.

Part of this aspect of the invention is the use of both visual andmathematical techniques for analyzing frequency domain transforms,including for example calculation of a harmonic magnitude ratio (HMR).To determine the HMR, a major peak or a central region of energydistribution in a spectrum of a frequency domain transform (such as anFFT) may be identified, and the HMR calculated as follows: A magnitudeof the transform in the region of the identified point is determined(e.g., by summing the magnitude of the transform at the identified pointand at surrounding points), and is summed with the correspondingmagnitude in the region of harmonic values of the frequency for theidentified point. This sum is divided by a total magnitude of thetransform for the entire signal; the ratio is defined as the HMR.

One method which is known for bringing a patient out of VF("defibrillating") is to administer an electric shock across thepatient's heart. This electric shock must generally have a substantialenergy, e.g. 10-20 joules, and may often cause tissue damage to thepatient even if it is successful in defibrillating the patient. Multipleshocks may be required, generally of increasing energy. Accordingly, itwould be advantageous to use a larger shock only when necessary, and itwould be advantageous to use as few shocks as possible.

Part of this aspect of the invention is the discovery that when theenergy peak 603 of the frequency-domain transform 602 is at a relativelylow frequency, a relatively low energy shock will generally suffice todefibrillate the patient. When the energy peak 603 of thefrequency-domain transform 602 is at a relatively high frequency (also,when a secondary energy peak 604 appears in the frequency-domaintransform 602 at a relatively high frequency), it will require arelatively high energy shock to defibrillate the patient, if it ispossible to defibrillate the patient by means of an electric shock atall.

One application of this discovery is in automated implanted cardiacdefibrillators (AICDs), which attempt to automatically detect VF and toautomatically administer a shock to defibrillate the patient.

FIG. 7 shows an improved AICD 701. A patient 702 is coupled to an AICDEKG 703, which acquires EKG signals and transmits them to an AICDprocessor 704, which controls a shock device 705 for administering adefibrillating shock to the patient 702.

The improved AICD 701 also comprises (e.g., as part of the AICDprocessor 704) software for determining an FFT of the EKG signal and fordetermining the energy peak in that FFT. If the energy peak in that FFTis relatively low, the AICD processor 704 controls the shock device 705to administer a relatively small shock to the patient. If the energypeak in that FFT is relatively high, the AICD processor 704 controls theshock device 705 to administer a relatively large shock to the patient,and may also signal an alarm 706 or other indicator that defibrillationmay not be successful.

III

A third aspect of the invention relates to detection and evaluation ofdrug toxicity based on a parameter time constant for an action-potentialduration (APD) restitution curve or an action-potential amplitude (APA)curve which is constructed for the patient.

FIG. 8 shows a signal response of an individual heart muscle cell to astimulus. This individual cell response is known in the art as "actionpotential".

It is well-known in the art that a time duration for recovery 801 of anindividual cell depends on factors including a resting period 802 whichthe cell has had prior to stimulus. It is also well-known in the artthat an APD restitution curve can be constructed for a human patientwith the use of an intracardiac catheter. However, the complete relationbetween the actual time duration for recovery 801 based on the restingperiod 802 is not known.

Part of this aspect of the invention is the discovery that when the timeduration for recovery 801 is plotted against the resting period 802(diastolic interval), the curve follows an exponential relation:

    APD=APD.sub.pL -A*exp(-DI/tau)                             (803)

where APDp_(pL) is the plateau APD, A is a proportionality constant, DIis the diastolic interval, and tau is the parameter time constant

The nonlinear nature of the APD restitution curve may promotedeterministic chaos in response to excessive stimulus of the heartmuscle cells. When the APD restitution curve is steeper (i.e., theparameter time constant tau is larger), there is accordingly a greaterpredilection for the heart to enter VF. Thus, another part of thisaspect of the invention is the discovery that a normal patient will havea relatively low APD restitution parameter time constant, while apatient who is exhibiting drug toxicity (e.g., quinidine intoxication)will have a relatively high APD restitution parameter time constant. Therestitution parameter time constant may also be used in monitoringcardiac stability, and in evaluating efficacy of antiarrhythmic drugs.

Experimental verification of the present invention has been achieved bythe inventors.

Experiment I

A mathematical study used PPPs, return maps, Poincare sections,correlation dimension, and spectral analysis to distinguish periodic,chaotic and random signals. PPPs were useful in distinguishing among allthree classes of signals. Periodic signals showed clear, widelyseparated trajectories; chaotic signals showed banding, forbidden zonesand sensitive dependence on initial conditions; random signals showed noclear internal structure. With the exception of noise effects, the onlymajor difference between the PPPs and the appropriately lagged returnmap was a 45 degree rotation. Poincare sections were also able todistinguish among the three classes of signals: periodic signals showedisolated points; chaotic signals showed ordered areas of apparentself-similarity; random signals showed a Gaussian distribution ofpoints. Correlation dimension was more able to distinguish betweenchaotic and random signals than between chaotic and periodic signals.Spectral analysis using FFTs and harmonic magnitude ratio (HMR) was ableto distinguish periodic signals, but were unable to distinguish betweenrandom and chaotic signals: HMRs of periodic signals were greater than97%; HMRs of chaotic signals varied between 17 and 80%; HMRs of randomsignals were approximately 40%. PPPs were greatly affected by noise,return maps were less affected, while spectral analysis was relativelyimmune to noise. It was concluded that PPPs, return maps, Poincaresections, correlation dimension and spectral analysis are all usefuldeterminatives of chaotic systems.

Experiment II

A mathematical study concentrated specifically on ability of spectralanalysis to distinguish chaotic from random signals. In this experiment,two series of random signals were generated. The first series comprised5000 pseudo-random numbers which were smoothed using a method ofleast-squares approximation. The second series comprised white noiseobtained from an analog-to-digital conversion board. Spectral analysiswas performed by applying an FFT to the data, and searching for a broadband spectrum or a change from a narrow band to a broad band, which waspresumed to be diagnostic of chaos. It was concluded that spectralanalysis by itself was insufficient to unequivocally distinguish chaoticsignals from random signals, and that additional tests such as PPPs andreturn maps were necessary for this purpose.

Experiment III

An experiment examined spectral analysis, visualization of PPPs andcorrelation dimension analysis, for usefulness in distinguishing betweennormal sinus rhythm and VF in dogs. Ischemia and re-perfusion were usedas stress factors in closed-chest anesthetized dogs. Spectral analysisof the dogs having normal sinus rhythm revealed narrow-band spectra withfundamental frequencies at the sinus rate and harmonics extending beyond50 Hz. PPPs were consistent with periodic dynamics, and dimensionanalysis revealed low dimensional behavior (1-2.5). In contrast,spectral analysis of the dogs having VF, revealed broad-band behaviorwith most of the energy at 6 Hz, and with energy at all frequenciesbetween 1 and 25 Hz. PPPs showed constrained aperiodic behavior, and thedimensional analysis revealed higher dimensions (4-6) than that observedfor the normal sinus rhythm dogs. Thus, all three techniques proveduseful in distinguishing normal sinus rhythm from VF.

Experiment IV

An experiment examined spectral analysis, visualization of PPPs,visualization of return maps, and correlation dimension analysis, fortheir usefulness in identifying VF in humans. These analyticaltechniques were applied to data from eight hypothermic patientsundergoing spontaneous VF, and also to data from three normothermicpatients with VF induced during electrophysiology testing. All patientshad a broad band frequency spectrum (0-12 Hz), a low dimension (range2-5), and banding and forbidden zones on PPPs and return maps. It wasconcluded that spectral analysis, visualization of PPPs, visualizationof return maps, and correlation dimension analysis are useful indetecting and evaluating VF.

Experiment V

An experiment examined spectral analysis, visualization of PPPs andcorrelation dimension analysis for their usefulness in distinguishingbetween normal sinus rhythm and VF in humans. VF in eight hypothermichuman patients undergoing open-heart surgery was studied. In allpatients, first and second order PPPs showed forbidden zones andbanding, and an FFT revealed a relatively continuous power spectrum atall frequencies from zero to 25 Hz, with a majority of the power below12 Hz. In contrast, correlation dimension in all cases was less than 4.It was concluded that multiphasic analysis of the data is preferable toreliance on a single analytical technique such as correlation dimension.

Experiment VI

An experiment utilized spectral analysis and visualization of PPPs toelucidate the heterogenous nature of atrial fibrillation. In theexperiment, the researchers induced acute fibrillation by a rapid trainof stimuli to the atria of seven closed-chested dogs. PPPs based on theEKG data often inscribed well defined structures, and an FFT of thedigitized EKGs showed peaks mostly below 15 Hz that were either discretewith clear harmonic components, or had continuous spectra that changedin a time- and site-dependent manner. It was concluded that bothspectral analysis and visualization of PPPs are useful techniques foranalyzing atrial as well as ventricular fibrillation.

Experiment VII

In an experiment, visual analysis of PPPs and the slope of an APDrestitution curve were found to be useful for detecting and evaluatingquinidine-induced VF in in vivo hearts. Quinidine was administered at 30minute intervals over five hours, until either a total of 90-100 mg/kgwas administered or until ventricular tachycardia or VF occurred,whichever came first. PPPs of the quinidine intoxicated cellsdemonstrated sensitive dependence on initial conditions and the presenceof forbidden zones, and the corresponding FFTs showed continuousspectra. In contrast, PPPs of cells in a control dog were uniform anddensely packed, and the corresponding FFTs showed discrete spectra. Theinitial slope of the APD restitution curve of quinidine intoxicatedcells was much steeper, by at least an order of magnitude, than theslope of normal cells. It was concluded that quinidine toxicitycorrelates with the slope of the APD restitution curves.

Experiment VIII

An experiment compared the slope of the APD and APA restitution curveswith quinidine intoxication. Quinidine was administered (90-100 mg/kg)to eight dogs over a five hour period. Three untreated dogs served ascontrols. Ventricular and Purkinje cells from both treated and untreateddogs were then subjected to electrical stimulation with cycles from 900to below 600 msec. Shortening of the cycle length to 600 msec resultedin irregular dynamics of both APD and APA, including electricalalternants and bifurcation. The slope of an APD restitution curve wascalculated, and found to be steeper in quinidine-intoxicated cells forboth Purkinje fibers and ventricular muscle cells than the slope duringquinidine washout or in normal untreated cells. The curve could be fitby the exponential equation given herein. APA changes were almost alwayscorrelated with the APD changes. In the three normal tissue preparationsneither ventricular muscle cells nor Purkinje cells showed bifurcativebehavior with respect to APD or AA. It was concluded that quinidinetoxicity, and presumably other drug-induced proarrhythmic effects,correlate with the slope of both APD and APA restitution curves.

Experiment IX

In an experiment, quinidine-induced ventricular tachycardia and VF indogs was analyzed using PPPs generated from action potential duration(APD) and action potential amplitude (APA) data. Both PPPs showedforbidden zones and sensitive dependence on initial conditions which areindicative of chaos. It was concluded that PPPs based on either APD orAPA are useful in detecting and evaluating quinidine toxicity.

Experiment X

In an experiment, EKGs of quinidine intoxicated dogs were analyzed byfrequency spectra, phase plane plots, Poincare sections, return maps andLyapunov exponents. In the control state and at therapeutic doses, PPPswere uniformly thick and showed no gaps, indicating that cycle-to-cyclevariation was due to normal biological "noise". But as the quinidinedose was increased to intermediate levels (40-50 mg/kg), PPPs showedclear non-uniform thickening, indicating sensitive dependence on initialconditions, and also showed marked banding (densely filled regionsseparated by divisions or gaps). At these intermediate doses, Lyapunovexponents became positive and Poincare return maps also indicatednonrandom chaos. At still higher doses, PPPs became more complex. In twodogs that did exhibit VF (and not in another) there was a significantchange in the PPP at the last pre-fibrillatory dose: the development ofa "funnel", a classic mechanism of chaos. Frequency spectra at allpre-fibrillatory doses were discrete, with peaks at a fundamentalfrequency and multiple harmonics. It was concluded that chaos does occurduring progressive quinidine intoxication, and that PPPs, and graphicand numeric analysis based on the PPPs, are better indicators of chaosthan frequency spectra.

Experiment XI

In an experiment, quinidine toxicity in dogs was analyzed using PPPsgenerated from APA and APD data. EKG recordings were made at variousdriving rates from 1000 to 500 msec. Increase in the driving rate from1000 to 500 msec caused the progressive appearance of higher orderperiodicities (period 3 and 4). Phase locking was seen with a stimulus(S) response (R) pattern repeating periodically in all 4 preparations atS:R ratios of 2:1, 5:3, 3:2. At faster drive rates aperiodic variationsin APA and APD were observed. A number of intermediate stages thatpresage chaos were also seen in the quinidine intoxicated fibers. Theseresults further demonstrate the usefulness of the methods of the presentinvention to detect both quinidine intoxication and precursor stages tointoxication.

Experiment XII

In an experiment, quinidine toxicity in dogs was analyzed using PPPsgenerated from APA and APD data. Electrical stimuli were used to drivecardiac tissue at various rates from 2000 to under 300 msec. Thesestimuli caused steady alternants (bifurcation) in APD and APA of 108±36msec and 12±9 millivolts respectively. Further increase in driving ratesgave rise to irregular dynamics. This transition was preceded by variousrepeating stimulus-response ratios (phase-locking) for up to fiftyconsecutive beats. No such dynamics could be induced in three nontreated (control) tissues. The APD restitution curve had significantly(p<0.05) steeper slope than six control fibers. Stimulus-responselatency remained constant at 6-9 msec. PPPs of the APDs during theirregular dynamics showed sensitive dependence on initial conditions andforbidden zones consistent with chaos theory. These results furtherdemonstrate the usefulness of the methods of the present invention todetect both quinidine intoxication and precursor stages to intoxication.

Experiment XIII

An experiment used spectral analysis, PPPs, Poincare sections, LyapunovExponents and dimension analysis to analyze computer simulated waveformsincluding sine waves, modulated sine waves, square waves, saw toothedwaves, and triangular waves. The researchers added random noise to thewaveforms at 1%, 10% and 20%. The experiment further used the sameanalytical techniques on EKG data from anesthetized dogs in which VF wasprecipitated by five different interventions: quinidine intoxication;premature electrical stimulation followed by quinidine intoxication;coronary occlusion; reperfusion of acutely ischemic myocardium; andglobal hypothermia. The preliminary results showed that PPPs andPoincare sections in dogs undergoing ventricular fibrillation wereconsistent with chaos, while spectral analysis was not suggestive ofchaos. The researchers concluded in part that VF can be described aschaotic electrophysiological behavior, but that single methods ofanalysis are not sufficient to detect such behavior.

IV

One conclusion which may be drawn from the research cited herein is thatthe analytical value of each of the aspects of the invention may beenhanced through combination with one or more of the other aspects ofthe invention. A preferred embodiment of the present invention mayinclude a combination of the aspects of the invention described herein.One preferred embodiment may comprise multiphasic analysis of a PPP(e.g., visually with a display, graphically with Poincare sections, andnumerically with Lyapunov exponents and correlation dimension),frequency spectral analysis, and mathematical analysis of an APDrestitution curve.

Alternative Embodiments

While preferred embodiments are disclosed herein, many variations arepossible which remain within the concept and scope of the invention, andthese variations would become clear to one of ordinary skill in the artafter perusal of the specification, drawings and claims herein.

It would also become clear to one of ordinary skill in the art thatembodiments of the invention may comprise means for continuousmonitoring of drug toxicity, atrial fibrillation, ischemia or otherheart conditions, such as during surgery or patient recovery fromsurgery. Moreover, embodiments of the invention may comprise means forindicating heart conditions which are detected to attending medicalpersonnel or to the patient. In one preferred embodiment of theinvention, means may be provided for directing the patient (when a heartdisorder is detected) to contact a physician or to proceed to a nearbyhospital for treatment. ##SPC1##

What is claimed is:
 1. A defibrillator comprisingmeans for acquiring anEKG signal; means for performing a frequency domain transform of saidEKG signal; means for calculating an energy peak for said frequencydomain transform; means for generating a shock energy to be applied to apatient; means for calculating the shock energy to be applied to apatient as a function of said energy peak of said frequency domaintransform; and a shock device for applying the shock energy to beapplied to a patient.
 2. A defibrillator as in claim 1 wherein saidmeans for calculating an energy peak for said frequency domain transformcalculates the energy peak by determining a harmonic magnitude ratio ofa region of energy distribution.
 3. A defibrillator as in claim 1wherein said defibrillator is an AICD defibrillator and said means forcalculating the shock energy to be applied to a patient is an AICDprocessor.
 4. A defibrillator as in claim 1 wherein said means foracquiring an EKG signal is an AICD EKG.
 5. A defibrillator comprisinganAICD EKG; an AICD processor which acquires EKG signals from said AICDEKG; said AICD processor further comprisinga FFT of an EKG signal; meansfor determining said FFT of said EKG signal; means for determining theenergy peak in said FFT; means for generating shock energy to be appliedto a patient; means for determining the shock energy to be applied to apatient as a function of said energy peak of said FFT; and a shockdevice for applying the shock energy to be applied to a patient.